Reducing the Dimension of Online Calibration in Dynamic Traffic Assignment Systems
Name
ReducingthedimensionofonlinecalibrationinDynamicTrafficAssignmentsystems.pdf
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1.33 MB
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Checksum (MD5)
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Author(s)
Seshadri, Ravi
Antoniou, Constantinos
Pereira, Francisco C.
Akkinepally, Arun P
Ben-Akiva, Moshe E
Date Issued
2017
Journal
Transportation Research Record: Journal of the Transportation Research Board
Publisher
SAGE Publications
Citation
Prakash, A. Arun et al. “Reducing the Dimension of Online Calibration in Dynamic Traffic Assignment Systems.” Transportation Research Record: Journal of the Transportation Research Board 2667 (January 2017): 96–107
Version
Author's final manuscript
Abstract
Effective real-time traffic management strategies often require dynamic traffic assignment systems that are calibrated online. But the computationally intensive nature of online calibration limits their application to smaller networks. This paper presents a dimensionality reduction of the online calibration problem that is based on principal components to overcome this limitation. To demonstrate this approach, the origin–destination flow estimation problem is formulated in relation to its principal components. The efficacy of the procedure was tested with real data on the Singapore Expressway network in an open-loop framework. A reduction in the problem dimension by a factor of 50 was observed with only a 2% loss in estimation accuracy. Further, the computational times were reduced by an order of 100. The procedure led to better predictions, as the principal components captured the structural spatial relationships. This work has the potential to make the online calibration problem more scalable.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3141/2667-10
